Insurance fraud costs the industry billions annually, with complex schemes getting more sophisticated. Yet many Special Investigation Units (SIUs) still use manual processes for a basic task: checking corporate entities and mapping who owns what.
The corporate search time-sink
Investigators spend too much of their valuable time on boring, repetitive work – manually searching business records, copying data into spreadsheets, and trying to map complex ownership structures. This process of “figuring out who owns a corporation, and then doing the officer search to find out what other corporations are associated with” has become a major bottleneck.
This creates several problems:
- Higher labor costs as skilled investigators waste time gathering basic data
- Slower investigation times affecting claim processing
- More errors and missed connections with manual work
- Limited ability to catch sophisticated fraud
- Compliance risks from incomplete ownership checks
SIU research shows that while automated referrals now account for 45% of total cases, manually checking corporate structures remains a stubborn problem that limits how much work investigators can do.
Why insurers keep postponing solutions
Despite clear pain points, many insurers put off implementing solutions for corporate data checks because of:
- Worries about how hard it will be to connect with existing systems
- Perceived high costs of new technology
- Other priorities in their technology plans
- Resistance to changing how investigations are done
- Uncertainty about which solution best fits their needs
The insurance industry faces strong pressure to control expenses, especially in claims departments. This pressure can conflict with the need to invest in technology that might not show immediate benefits, even when long-term gains are clear.
How top insurers are pulling ahead
Leading insurers are quickly changing how they detect and investigate fraud. The biggest industry trends include:
- Using Artificial Intelligence (AI) and Machine Learning (ML) to spot complex fraud patterns
- More automation for repetitive verification tasks
- Connecting various third-party data sources via APIs to enhance internal data
These technologies are delivering real results. Insurers using ML-based fraud detection report savings of 15-25% ($1M-$3M annually), while those using big data analytics for fraud prevention have investigation costs 1.4 times lower than peers.
Automating Know Your Customer (KYC) checks, which is similar to corporate verification, shows impressive efficiency gains. Studies show reductions for consumer checks, commercial checks, and manual work time.
Your automation roadmap
Forward-thinking insurers start by targeting specific high-impact processes with clear efficiency potential. For corporate verification, this typically begins with a focused pilot program in the SIU.
The initial implementation usually involves:
- Choosing a specialized corporate data API solution that provides access to verified company information
- Identifying the main integration point (usually the SIU case management system)
- Setting clear metrics to measure time savings and investigation impact
- Training investigators on the new workflow
- Collecting feedback for ongoing improvement
The most successful implementations focus on fixing a specific pain point rather than trying to transform everything at once.
4 challenges you’ll face (and how to overcome them)
Several common obstacles appear during integration:
Technical integration complexity: Large insurers often use a mix of modern and legacy systems. Successful implementations use strategies like API wrappers, middleware platforms, or operational data stores to bridge technology gaps.
Data standardization: Mapping data fields from external APIs to internal systems needs careful planning to maintain data quality.
Change management: Getting investigators to adopt the new system is crucial. The best implementations involve SIU staff early to gather requirements and build advocates.
Security and compliance: Strong security measures are essential, including API authentication, secure data transmission (HTTPS), and following specific security policies.
APIs against corporate fraud
The most effective approach to solving the corporate verification challenge involves implementing an API-driven solution that provides direct access to structured, standardized legal entity data from primary, official sources.
This solution should:
- Enable programmatic access to business registry data
- Integrate smoothly with existing SIU workflows
- Provide clear data sources for audit and compliance purposes
- Deliver consistent, standardized data across jurisdictions
- Support relationship mapping to uncover hidden connections
For maximum value, this corporate data layer should be accessible at multiple points across the organization, including SIU case management, claims intake/triage, underwriting platforms, and analytics environments.
Connecting fragmented corporate records
The biggest challenge in this transformation is addressing the fragmentation of corporate data across many different sources. This fragmentation creates both technical and operational barriers to effective investigation.
Leading organizations overcome this by:
- Implementing a unified API solution that brings together data from multiple registries
- Creating a consistent data model that standardizes information across jurisdictions
- Developing visualization tools for complex ownership structures
- Establishing clear workflows for escalating cases needing deeper investigation
- Building connections between investigation systems and core claims platforms
Companies that successfully tackle this challenge report dramatic improvements in investigation efficiency and effectiveness.
Show me the ROI
Organizations that successfully implement corporate data automation solutions report substantial ROI across multiple areas:
Operational efficiency:
- Major reduction in manual investigator time
- Faster average investigation cycle times
- Increased case handling capacity
- Fewer errors and less rework
Enhanced detection capabilities:
- Better identification of complex fraud schemes
- Improved detection of organized fraud networks
- More consistent and accurate risk assessment
Compliance improvements:
- More reliable Ultimate Beneficial Owner (UBO) identification
- Better adherence to Know Your Customer (KYC) regulations
- Reduced regulatory risk
Enterprise-wide value:
- Streamlined workflows in underwriting and claims
- Improved data consistency across the organization
- Enhanced analytical capabilities for fraud pattern detection
Spreading your automation success company-wide
As the solution becomes established, leading organizations extend its value by:
- Expanding access to other departments (underwriting, claims, compliance)
- Integrating the data with advanced analytics platforms for pattern detection
- Building automated alerts for changes in corporate structures
- Developing more sophisticated network analysis capabilities
- Creating dashboards to track investigation efficiency metrics
This phased expansion approach maximizes ROI while minimizing implementation risk.
Transforming your SIU team
Organizations that effectively solve the corporate verification challenge achieve more than just operational efficiency. They fundamentally transform their fraud detection and risk management capabilities.
With automated access to accurate corporate data, investigators shift from tedious data gathering to strategic analysis. This enables:
- Faster identification of suspicious patterns
- More thorough investigation of complex cases
- Stronger evidence for fraudulent claim denials
- Better allocation of investigative resources
- Improved risk selection in underwriting
The ability to easily map corporate relationships and ownership structures provides a crucial competitive edge in detecting sophisticated fraud schemes that might otherwise go unnoticed.
6 steps to kick-start your automation journey
For insurers looking to address the corporate verification bottleneck, consider these practical next steps:
- Assess your current manual processes, measuring time spent on corporate lookups
- Identify integration points within existing technology infrastructure
- Evaluate corporate data API solutions, focusing on data quality, coverage, and integration flexibility
- Plan a focused pilot within the SIU to demonstrate value
- Establish clear metrics to track ROI
- Develop a phased implementation roadmap that extends access across the organization
By taking a strategic approach to automating corporate data verification, insurers can transform a manual bottleneck into a competitive advantage, driving both operational efficiency and enhanced fraud detection capabilities.
For more information
Learn more about how OpenCorporates’ data can help you understand corporate structures and manage risk. Reach out for a demo or explore our services.
